




import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


lowerCAmelCase_						 =					re.compile('[^A-Za-z_0-9]')
# parameters used in DuplicationIndex
lowerCAmelCase_						 =					10
lowerCAmelCase_						 =					2_56






def    snake_case(							__magic_name__       )					->				Dict:





     '''simple docstring'''


     if len(a_       ) < MIN_NUM_TOKENS:
          return None
     lowercase   :  Tuple        =					MinHash(num_perm=a_       )
     for token in set(a_       ):
          min_hash.update(token.encode()       )
     return min_hash






def    snake_case(							__magic_name__       )					->				Optional[int]:





     '''simple docstring'''


     return {t for t in NON_ALPHA.split(a_       ) if len(t.strip()       ) > 0}
class 		_A   :






  def __init__(    self						:      Dict ,		*,
      _A						:      Dict = 0.85 ,		)						->       List[str]:






       """simple docstring"""







       lowercase   :  List[str]        =					duplication_jaccard_threshold
       lowercase   :  Optional[int]        =					NUM_PERM
       lowercase   :  Optional[int]        =					MinHashLSH(threshold=self._duplication_jaccard_threshold ,		num_perm=self._num_perm )

       lowercase   :  List[Any]        =					defaultdict(_A )






  def    __a     (    self						:      str ,		_A						:      str ,		_A						:      Any )						->       None:






       """simple docstring"""







       lowercase   :  int        =					self._index.query(_A )
       if code_key in self._index.keys:
            print(f"""Duplicate key {code_key}""" )
            return

       self._index.insert(_A ,		_A )
       if len(_A ) > 0:
            for base_duplicate in close_duplicates:
                 if base_duplicate in self._duplicate_clusters:
                      self._duplicate_clusters[base_duplicate].add(_A )
                      break
            else:
                 self._duplicate_clusters[close_duplicates[0]].add(_A )






  def    __a     (    self						:      List[Any] )						->       List[List[Dict]]:






       """simple docstring"""







       lowercase   :  Tuple        =					[]
       for base, duplicates in self._duplicate_clusters.items():
            lowercase   :  Union[str, Any]        =					[base] + list(_A )
            # reformat the cluster to be a list of dict
            lowercase   :  Union[str, Any]        =					[{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
            duplicate_clusters.append(_A )
       return duplicate_clusters






  def    __a     (    self						:      Any ,		_A						:      Union[str, Any] )						->       None:






       """simple docstring"""







       lowercase   :  Any        =					self.get_duplicate_clusters()
       with open(_A ,		'''w''' ) as f:
            json.dump(_A ,		_A )






def    snake_case(							__magic_name__       )					->				Any:





     '''simple docstring'''


     lowercase     ,			lowercase   :  Dict        =					element
     lowercase   :  Tuple        =					get_min_hash([t for t in NON_ALPHA.split(data['''content''']       ) if len(t.strip()       ) > 0]       )
     if min_hash is not None:
          return (index, data["repo_name"], data["path"]), min_hash






def    snake_case(							__magic_name__       )					->				List[Any]:





     '''simple docstring'''


     with mp.Pool() as pool:
          for data in pool.imap_unordered(
              _compute_min_hash					,						ThreadedIterator(a_					,						max_queue_size=1_00_00       )					,						chunksize=1_00					,						):
               if data is not None:
                    yield data






def    snake_case(							__magic_name__					,						__magic_name__       )					->				List[str]:





     '''simple docstring'''


     lowercase   :  Dict        =					DuplicationIndex(duplication_jaccard_threshold=a_       )

     for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(a_       )       )					,						max_queue_size=1_00       )       ):
          di.add(a_					,						a_       )

     # Returns a List[Cluster] where Cluster is List[str] with the filenames.
     return di.get_duplicate_clusters()






def    snake_case(							__magic_name__					,						__magic_name__       )					->				List[str]:





     '''simple docstring'''


     lowercase   :  Tuple        =					get_tokens(a_       )
     lowercase   :  Optional[Any]        =					get_tokens(a_       )
     return len(tokensa & tokensa       ) / len(tokensa | tokensa       )


lowerCAmelCase_						 =					None






def    snake_case(							__magic_name__					,						__magic_name__       )					->				int:





     '''simple docstring'''


     lowercase   :  str        =					[]
     for elementa in cluster:
          lowercase   :  Optional[int]        =					_shared_dataset[elementa['''base_index''']]['''content''']
          for elementa in extremes:
               lowercase   :  int        =					_shared_dataset[elementa['''base_index''']]['''content''']
               if jaccard_similarity(a_					,						a_       ) >= jaccard_threshold:
                    elementa["copies"] += 1
                    break
          else:
               lowercase   :  int        =					1
               extremes.append(a_       )
     return extremes






def    snake_case(							__magic_name__					,						__magic_name__					,						__magic_name__       )					->				List[Any]:





     '''simple docstring'''


     global _shared_dataset
     lowercase   :  str        =					dataset
     lowercase   :  List[Any]        =					[]
     lowercase   :  str        =					partial(_find_cluster_extremes_shared					,						jaccard_threshold=a_       )
     with mp.Pool() as pool:
          for extremes in tqdm(
              pool.imap_unordered(
                  a_					,						a_					,						)					,						total=len(a_       )					,						):
               extremes_list.append(a_       )
     return extremes_list






def    snake_case(							__magic_name__					,						__magic_name__ = 0.8_5       )					->				int:





     '''simple docstring'''


     lowercase   :  Union[str, Any]        =					make_duplicate_clusters(a_					,						a_       )
     lowercase   :  str        =					{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
     lowercase   :  Dict        =					{}
     lowercase   :  List[str]        =					find_extremes(a_					,						a_					,						a_       )
     for extremes in extremes_clusters:
          for element in extremes:
               lowercase   :  List[str]        =					element
     lowercase   :  str        =					duplicate_indices - set(extreme_dict.keys()       )
     lowercase   :  int        =					dataset.filter(lambda __magic_name__					,						__magic_name__       : idx not in remove_indices					,						with_indices=a_       )

     # update duplicate_clusters
     for cluster in duplicate_clusters:
          for element in cluster:
               lowercase   :  Dict        =					element['''base_index'''] in extreme_dict
               if element["is_extreme"]:
                    lowercase   :  Dict        =					extreme_dict[element['''base_index''']]['''copies''']

     print(F"""Original dataset size: {len(a_       )}"""       )
     print(F"""Number of duplicate clusters: {len(a_       )}"""       )
     print(F"""Files in duplicate cluster: {len(a_       )}"""       )
     print(F"""Unique files in duplicate cluster: {len(a_       )}"""       )
     print(F"""Filtered dataset size: {len(a_       )}"""       )

     return ds_filter, duplicate_clusters





'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TrajectoryTransformerModel",
      "TrajectoryTransformerPreTrainedModel",
      "load_tf_weights_in_trajectory_transformer",
  ]


if TYPE_CHECKING:
  from .configuration_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TrajectoryTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TrajectoryTransformerModel,
        TrajectoryTransformerPreTrainedModel,
        load_tf_weights_in_trajectory_transformer,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



